Facilitation of efficient software downloads for vehicles

ABSTRACT

A more efficient over-the-air software push can be facilitated by leveraging a smart scheduling system for vehicles. The smart scheduling system can use location and network capacity data to prioritize over-the-air software pushes for vehicles. For instance, a vehicle, which is only operational during off-peak wireless network hours can receive a software push during the off-peak times because wireless network capacity is not an issue. However, vehicles, which are used primarily during heavy peak wireless network times can receive software in a prioritized manner based on location data, frequency of use, network capacity, etc.

RELATED APPLICATION

The subject patent application is a continuation of, and claims priorityto, U.S. patent application Ser. No. 15/193,685 (now U.S. Pat. No.10,470,189), filed Jun. 27, 2016, and entitled “FACILITATION OFEFFICIENT SOFTWARE DOWNLOADS FOR VEHICLES,” the entirety of whichapplication is hereby incorporated by reference herein.

TECHNICAL FIELD

This disclosure generally relates to facilitating software downloads forvehicles. More specifically, this disclosure relates to scheduledover-the-air downloads for vehicles based on efficient modelingpatterns.

BACKGROUND

Over-the-air programming (OTA) or firmware OTA (FOTA) refers to variousmethods of distributing new software, configuring settings, and updatingencryption keys to devices like cellphones, set-top boxes or securevoice communication equipment (encrypted 2-way radios). One feature ofOTA is that one central location can send an update to all the users,who are unable to refuse, defeat, or alter that update, and that theupdate applies immediately to everyone on the channel.

In the context of the mobile content world these can compriseover-the-air service provisioning (OTASP), over-the-air provisioning(OTAP) or over-the-air parameter administration (OTAPA), or provisioninghandsets with the necessary settings with which to access services suchas wireless application protocols (WAP) or multimedia messaging services(MMS).

As mobile devices accumulate new applications and become more advanced,OTA configuration has become increasingly important as new updates andservices come on stream. OTA via short messaging services (SMS)optimizes the configuration data updates in SIM cards and handsets andenables the distribution of new software updates to mobile devices orprovisioning handsets with the necessary settings with which to accessservices such as WAP or MMS. OTA messaging provides remote control ofmobile devices for service and subscription activation, personalizationand programming of a new service for mobile operators andtelecommunication third parties.

A connected car is a car that is equipped with Internet access, andusually also with a wireless area network. This allows the car to shareinternet access with other devices both inside as well as outside thevehicle. Often, the car is also outfitted with special technologies thattap into the internet or wireless area network (LAN) and provideadditional benefits to the driver. Examples include: automaticnotification of crashes, notification of speeding and safety alerts,etc. Concierge features provided by automakers or apps can alert thedriver of the time to leave to arrive on time from a calendar, send textmessage alerts to friends or business associates to alert them ofarrival times, and/or help find parking or gas stations.

Connected cars have become a more dominant presence in the network.While the average lifespan of a smartphone is 21 months, a car on theaverage is scrapped after 8 or more years. Therefore, in order to allowcustomers to keep up with technological evolution, software updates,which can be facilitated OTA can generate efficiencies.

The above-described background relating to efficient software downloadfor vehicles is merely intended to provide a contextual overview of somecurrent issues, and is not intended to be exhaustive. Other contextualinformation may become further apparent upon review of the followingdetailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

Non-limiting and non-exhaustive embodiments of the subject disclosureare described with reference to the following figures, wherein likereference numerals refer to like parts throughout the various viewsunless otherwise specified.

FIG. 1 illustrates an example wireless network comprising a vehiclecommunicating with the wireless network according to one or moreembodiments.

FIG. 2 illustrates an example wireless network performing OTA softwarepushes to vehicles based on their location according to one or moreembodiments.

FIG. 3 illustrates a learning procedure of an example wireless networkperforming OTA software scheduling according to one or more embodiments.

FIG. 4 illustrates an example schematic system block diagram of an OTAscheduling process according to one or more embodiments.

FIG. 5 illustrates an example wireless network architecture for OTAsoftware scheduling for a vehicle according to one or more embodiments.

FIG. 6 illustrates an example wireless network architecture for OTAsoftware scheduling for a vehicle according to one or more embodiments.

FIG. 7 illustrates an example schematic system block diagram for a OTAsoftware scheduler component according to one or more embodiments.

FIG. 8 illustrates an example schematic system block diagram forfacilitating OTA software based on resource data according to one ormore embodiments.

FIG. 9 illustrates an example schematic system block diagram forfacilitating OTA software based on a segmentation process according toone or more embodiments.

FIG. 10 illustrates an example schematic system block for facilitatingOTA software based on an adaptive scheduling process according to one ormore embodiments.

FIG. 11 illustrates an example block diagram of an example mobilehandset operable to engage in a system architecture that facilitatessecure wireless communication according to one or more embodimentsdescribed herein.

FIG. 12 illustrates an example block diagram of an example computeroperable to engage in a system architecture that facilitates securewireless communication according to one or more embodiments describedherein.

DETAILED DESCRIPTION

In the following description, numerous specific details are set forth toprovide a thorough understanding of various embodiments. One skilled inthe relevant art will recognize, however, that the techniques describedherein can be practiced without one or more of the specific details, orwith other methods, components, materials, etc. In other instances,well-known structures, materials, or operations are not shown ordescribed in detail to avoid obscuring certain aspects.

Reference throughout this specification to “one embodiment,” or “anembodiment,” means that a particular feature, structure, orcharacteristic described in connection with the embodiment is includedin at least one embodiment. Thus, the appearances of the phrase “in oneembodiment,” “in one aspect,” or “in an embodiment,” in various placesthroughout this specification are not necessarily all referring to thesame embodiment. Furthermore, the particular features, structures, orcharacteristics may be combined in any suitable manner in one or moreembodiments.

As utilized herein, terms “component,” “system,” “interface,” and thelike are intended to refer to a computer-related entity, hardware,software (e.g., in execution), and/or firmware. For example, a componentcan be a processor, a process running on a processor, an object, anexecutable, a program, a storage device, and/or a computer. By way ofillustration, an application running on a server and the server can be acomponent. One or more components can reside within a process, and acomponent can be localized on one computer and/or distributed betweentwo or more computers.

Further, these components can execute from various machine-readablemedia having various data structures stored thereon. The components cancommunicate via local and/or remote processes such as in accordance witha signal having one or more data packets (e.g., data from one componentinteracting with another component in a local system, distributedsystem, and/or across a network, e.g., the Internet, a local areanetwork, a wide area network, etc. with other systems via the signal).

As another example, a component can be an apparatus with specificfunctionality provided by mechanical parts operated by electric orelectronic circuitry; the electric or electronic circuitry can beoperated by a software application or a firmware application executed byone or more processors; the one or more processors can be internal orexternal to the apparatus and can execute at least a part of thesoftware or firmware application. As yet another example, a componentcan be an apparatus that provides specific functionality throughelectronic components without mechanical parts; the electroniccomponents can include one or more processors therein to executesoftware and/or firmware that confer(s), at least in part, thefunctionality of the electronic components. In an aspect, a componentcan emulate an electronic component via a virtual machine, e.g., withina cloud computing system.

The words “exemplary” and/or “demonstrative” are used herein to meanserving as an example, instance, or illustration. For the avoidance ofdoubt, the subject matter disclosed herein is not limited by suchexamples. In addition, any aspect or design described herein as“exemplary” and/or “demonstrative” is not necessarily to be construed aspreferred or advantageous over other aspects or designs, nor is it meantto preclude equivalent exemplary structures and techniques known tothose of ordinary skill in the art. Furthermore, to the extent that theterms “includes,” “has,” “contains,” and other similar words are used ineither the detailed description or the claims, such terms are intendedto be inclusive—in a manner similar to the term “comprising” as an opentransition word—without precluding any additional or other elements.

As used herein, the term “infer” or “inference” refers generally to theprocess of reasoning about, or inferring states of, the system,environment, user, and/or intent from a set of observations as capturedvia events and/or data. Captured data and events can include user data,device data, environment data, data from sensors, sensor data,application data, implicit data, explicit data, etc. Inference can beemployed to identify a specific context or action, or can generate aprobability distribution over states of interest based on aconsideration of data and events, for example.

Inference can also refer to techniques employed for composinghigher-level events from a set of events and/or data. Such inferenceresults in the construction of new events or actions from a set ofobserved events and/or stored event data, whether the events arecorrelated in close temporal proximity, and whether the events and datacome from one or several event and data sources. Various classificationschemes and/or systems (e.g., support vector machines, neural networks,expert systems, Bayesian belief networks, fuzzy logic, and data fusionengines) can be employed in connection with performing automatic and/orinferred action in connection with the disclosed subject matter.

In addition, the disclosed subject matter can be implemented as amethod, apparatus, or article of manufacture using standard programmingand/or engineering techniques to produce software, firmware, hardware,or any combination thereof to control a computer to implement thedisclosed subject matter. The term “article of manufacture” as usedherein is intended to encompass a computer program accessible from anycomputer-readable device, computer-readable carrier, orcomputer-readable media. For example, computer-readable media caninclude, but are not limited to, a magnetic storage device, e.g., harddisk; floppy disk; magnetic strip(s); an optical disk (e.g., compactdisk (CD), a digital video disc (DVD), a Blu-ray Disc™ (BD)); a smartcard; a flash memory device (e.g., card, stick, key drive); and/or avirtual device that emulates a storage device and/or any of the abovecomputer-readable media.

As an overview, various embodiments are described herein to facilitateOTA software pushes from wireless networks to vehicles.

For simplicity of explanation, the methods (or algorithms) are depictedand described as a series of acts. It is to be understood andappreciated that the various embodiments are not limited by the actsillustrated and/or by the order of acts. For example, acts can occur invarious orders and/or concurrently, and with other acts not presented ordescribed herein. Furthermore, not all illustrated acts may be requiredto implement the methods. In addition, the methods could alternativelybe represented as a series of interrelated states via a state diagram orevents. Additionally, the methods described hereafter are capable ofbeing stored on an article of manufacture (e.g., a machine-readablestorage medium to facilitate transporting and transferring suchmethodologies to computers. The term article of manufacture, as usedherein, is intended to encompass a computer program accessible from anycomputer-readable device, carrier, or media, including a non-transitorymachine-readable storage medium.

It is noted that although various aspects and embodiments are discussedherein with respect to Universal Mobile Telecommunications System (UMTS)and/or Long Term Evolution (LTE), the disclosed aspects are not limitedto a UMTS implementation and/or an LTE implementation. For example,aspects or features of the disclosed embodiments can be exploited insubstantially any wireless communication technology. Such wirelesscommunication technologies can include UMTS, Code Division MultipleAccess (CDMA), Wi-Fi, Worldwide Interoperability for Microwave Access(WiMAX), General Packet Radio Service (GPRS), Enhanced GPRS, ThirdGeneration Partnership Project (3GPP), LTE, Third Generation PartnershipProject 2 (3GPP2) Ultra Mobile Broadband (UMB), High Speed Packet Access(HSPA), Evolved High Speed Packet Access (HSPA+), High-Speed DownlinkPacket Access (HSDPA), High-Speed Uplink Packet Access (HSUPA), Zigbee,or another IEEE 802.XX technology. Additionally, substantially allaspects disclosed herein can be exploited in legacy telecommunicationtechnologies.

Described herein are systems, methods, articles of manufacture, andother embodiments or implementations that can facilitate OTA softwarepushes for vehicles. Facilitating OTA software pushes can be implementedin connection with any type of vehicle with a connection to thecommunications network.

Device-level location analytics coupled with network load andperformance metrics at a radio level can be used to create an optimizedschedule for an OTA software downloads. Location analytics can beemployed to create car segments with similar scheduling characteristics(commute vehicles, weekend vehicles, etc.) and then apply heuristicscheduling to the segments with less network impact, leaving the highimpact segments for the optimization. This can result in a reducedproblem space for the optimization process and less extensivecomputation while still producing a highly efficient schedule forsoftware updates.

A scheduler application program interface (API) can be a web servicethat receives input about an OTA campaign, comprising campaigntime-frame, targeted devices, locations, etc. It can be the entry pointto the scheduling process initiated by any standard web-based service.Location analytics can comprises session data collected at a resolutionof radio sessions. These call detail records can provide locationinformation at the radio level and can telemetrize data sessions. Inorder to identify the records for a class of cars, an access point name(APN) can be used as a filter.

In general, connected cars can be provisioned into APNs for eachoriginal equipment manufacturer (OEM). Each relevant record can comprisethe international mobile subscriber identity (IMSI) for the vehicle, thestart time, a list of radios that the vehicle was connected to, and theduration of each radio session. Sessions incurred by the relevant OEMcan be stored for each day in a file and sorted by the IMSI based on aleading field. Due to the large volume of data, the IMSI can allow quickaccess to the data.

A smart OTA software scheduler can rely on network metrics to scheduledownloads around peak network hours to minimize impact on other users.Network metrics can continuously be collected from the radio network. Animportant indicator can be the load level of each location (e.g. celltower or cell sector). For example, a physical resource block (PRB)utilization level (expressed as a percentage) can exist for an LTEnetwork, but another metric can be used for LTE or another technology.In addition, a PRB utilization level can be expressed relative to theamount of data transferred on a downlink, specifically for each celltower (or sector) because each cell tower can have different capacity interms of radio carriers or bandwidth. This can allow for computation ofhow much additional data (FOTA traffic) the cell tower can sustainbefore exceeding a pre-determined load (e.g. 80% PRB utilization). Forexample, 25% PRB utilization for 1 GB of data can mean that addinganother 1 GB of FOTA traffic would increase total PRB utilization to50%. In addition, different user populations and other conditions canimpact the use of each cell tower on a daily and weekly basis, so anetwork utilization model can be produced for each of the days of theweek. In some cases, it may be sufficient to produce a model for aweekday vs. weekend day or simply use the single model for each day,depending on circumstances. A usage model can also be created for each15-minute bin, but an hourly model, or any other time interval can beused as appropriate. As network usage evolves over time, the model canbe updated to include up to K most recent weeks.

Since schedule optimization can be computationally expensive, vehiclesegmentation can be used to segment the cars into groups to reducecosts. Some of the groups can be scheduled immediately, thereby reducingthe number of cars sent to the optimizer. Additionally, analysis of eachvehicle's past network sessions can be used to estimate relevantproperties including, but not limited to: vehicles frequency on anetwork, repetitive behavior of vehicles, and/or vehicles used onlyduring commute times. For instance, vehicles can exhibit strongrepetitive behavior on a daily or weekly level. For example, if avehicle is used every Sunday morning, or is only ever driven at night,updates can be scheduled during these times because they are off-peakfor the network load. It should be noted that off-peak time can be anytime where the network load is less than a peak network load.Conversely, vehicles that are only used at commute times or are onlyseen on the busiest radios could be difficult to schedule, so they mightpossibly have priority for scheduling by the optimizer. Theaforementioned properties others can be used to assign the vehicles tosegments.

A heuristic schedule can be based on the car segmentation, or even on arandom assignment of vehicles to download times within the campaign.Once a vehicle starts its download, it can continue to receive theupdates whenever it is connected to a radio access network (RAN), untileither it is complete or the campaign window ends. A simulation tool canbe used to compare a heuristic schedule with an optimized schedule. Thesimulation tool can quantify the degree of additional stress on the RANwhen the schedule is not optimized, as well as the number of extravehicles that will fail to complete the download under non-optimalconditions.

A schedule optimizer can use the metrics from the location data. Theoptimizer can be composed of two components: 1) a learning machine and2) a scheduler. The learning machine can provide several vehicles'orderings/permutations to be used to build the schedule. After theschedule has been built for the scheduler, a makespan, which is a totallength of the schedule, or other chosen metric can be fed back into thelearning machine to be used as a performance metric in the learningprocess. For example, a biased random-key genetic algorithm can be usedas the learning machine, but any package providing the same orsubstantially similar functionality can work. It should be noted thatother genetic algorithms (considered as machine-learning algorithms) andother types of optimization frameworks can be used including, but notlimited to: tabu search, simulated annealing, swarm optimization (antcolony optimization, particle swarm optimization, and other variants),variable neighborhood search, greedy randomized adaptive searchprocedures, branch-and-bound, branch-and-cut, branch-and-price (andtheir combinations), robust optimization. The scheduler can work with alist of vehicles in a given order provided by the learning machine. Foreach vehicle in this order, the schedule can allocate a download slotfor that vehicle constrained to the network metrics (for example,maximum utilization of PRBs in a given sector or radio). The result is alist of vehicles with the times when these vehicles should start thedownload. The makespan and other metrics can also be computed andreturned to the learning machine. Note that since the scheduling processcan be applied to independent vehicle orderings, it can be done inparallel. This process can be iterated several times until a givenstopping criterion is met. The stopping criteria can be maximum runningtime and convergence detection (a given number of iterations withoutimprovement in the solution).

The last process in the pipeline can be the gathering of output from theheuristic scheduler and optimizing scheduler into a campaign schedule inan extensible markup language (XML). This schedule can be communicatedto the campaign manager using the API.

It should also be noted that an artificial intelligence (AI) componentcan facilitate automating one or more features in accordance with thedisclosed aspects. A memory and a processor as well as other componentscan include functionality with regard to the figures. The disclosedaspects in connection with OTA software pushes for vehicles can employvarious AI-based schemes for carrying out various aspects thereof. Forexample, a process for detecting one or more trigger events, reducing asoftware push as a result of the one or more trigger events, andmodifying one or more reported measurements, and so forth, can befacilitated with an example automatic classifier system and process.

An example classifier can be a function that maps an input attributevector, x=(x1, x2, x3, x4, xn), to a confidence that the input belongsto a class, that is, f(x)=confidence(class). Such classification canemploy a probabilistic and/or statistical-based analysis (e.g.,factoring into the analysis utilities and costs) to prognose or infer anaction that can be automatically performed. In the case of OTA softwarepushes, for example, attributes can be a frequency band and a technologyand the classes can be an output network capacity reduction value.

A support vector machine (SVM) is an example of a classifier that can beemployed. The SVM can operate by finding a hypersurface in the space ofpossible inputs, which the hypersurface attempts to split the triggeringcriteria from the non-triggering events. Intuitively, this makes theclassification correct for testing data that is near, but not identicalto training data. Other directed and undirected model classificationapproaches include, for example, naïve Bayes, Bayesian networks,decision trees, neural networks, fuzzy logic models, and probabilisticclassification models providing different patterns of independence canbe employed. Classification as used herein also may be inclusive ofstatistical regression that is utilized to develop models of priority.

The disclosed aspects can employ classifiers that are explicitly trained(e.g., via a generic training data) as well as implicitly trained (e.g.,via observing mobile device usage as it relates to triggering events,observing network frequency/technology, receiving extrinsic information,and so on). For example, SVMs can be configured via a learning ortraining phase within a classifier constructor and feature selectionmodule. Thus, the classifier(s) can be used to automatically learn andperform a number of functions, including but not limited to modifying asoftware push, modifying one or more reported mobility measurements, andso forth. The criteria can include, but is not limited to, predefinedvalues, frequency attenuation tables or other parameters, serviceprovider preferences and/or policies, and so on.

In one embodiment, described herein is a method comprising receivingnetwork capacity data representing a network capacity of network devicesof a wireless network, and receiving vehicle location data associatedwith a location of a vehicle in relation to a base station device of thewireless network. Thereafter, network connection data associated with afrequency of a radio of the vehicle connecting to the base stationdevice can be received, and based on the network resource data, thevehicle location, and the network connection data, generating, by thewireless network device, resource schedule data associated with aschedule for a resource deliverable by the wireless network device to besent to the radio of the vehicle.

According to another embodiment, a system can facilitate, the OTAsoftware pushes by receiving location data associated with locations ofvehicles in relation to a base station device associated with a wirelessnetwork, and receiving connection data associated with wirelessconnections of the vehicles to the base station device. Based on thelocation data and the connection data, segmenting the vehicles,resulting in a first vehicle segment and a second vehicle segment. Inresponse to the segmenting the vehicles, generating a firmware pushschedule associated with pushing firmware to the vehicles, and sendingthe firmware to the vehicles in accordance with the firmware pushschedule. It should be noted that although a vehicle segment cancomprise multiple vehicles, the firmware push does not necessarily needto be performed at the same time for all of the vehicles within thevehicle segment. For instance, a firmware push can be performed atvarious times for each vehicle within the vehicle segment.

According to yet another embodiment, described herein is amachine-readable storage medium that can perform the operationscomprising generating a first defined order associated with vehiclesconnected to network devices of a wireless network, and allocating firsttime slots related to the first defined order. Based on allocating thefirst time slots, generating a first schedule associated with sendingfirmware to the vehicles. In response to sending the firmware to thevehicles in accordance with first schedule, analyzing networkperformance data associated with a performance of the network devices ofthe wireless network, resulting in a network analysis. Based on anoutput of the network analysis, generating a second defined orderassociated with the vehicles, and allocating second time slots relatedto the second defined order. Based on the allocating the second timeslots, generating a second schedule associated with sending the firmwareto the vehicles, and sending the firmware to the vehicles in accordancewith the second schedule to increase a metric relating to theperformance of the network devices of the wireless network.

These and other embodiments or implementations are described in moredetail below with reference to the drawings.

Referring now to FIG. 1, illustrated is an example wireless networkcomprising a vehicle communicating with the wireless network accordingto one or more embodiments. A wireless network can comprise a datarepository 100 that can communicate with base stations 102, 104. Thebase stations 102, 104 can also communicate with a vehicle 106. Datarelated to how much time the vehicle spends communicating with basestations 102, 104 can be used to build a scheduling system for OTAsoftware pushes to the vehicle 106. For example, the system can notethat the vehicle 106 spends 60% of its time communicating with the basestation 104 and only 40% of its time communicating with the base station102 during its daily commute time. This information can be stored in thedata repository 100.

Additional information regarding network capacity can also be stored inthe data repository 100. For example, based on common traffic patterns,the system can note that the base station 104 is generally at 85%capacity during the daily commute time where the base station 102 isonly 40% capacity during the daily commute time. Consequently, based onan analysis of this data by the system, the system can determine that itmay be more efficient to provide OTA software pushes to the vehicle 106during the latter portion of its commute while it is communicating withthe base station 102 to mitigate the possibility of the OTA softwarepush bringing the base station 102 to its capacity. It should be notedthat this dynamic analysis can be performed for several vehicles andbase stations simultaneously to generate the most efficient outcome forthe OTA software push. Multiple vehicles can also be segmented intogroups based on a similar analysis where the segmentation can take intoaccount the vehicles that normally communicate with the base stations104, 102 with approximately a 60%/40% split.

Referring now to FIG. 2, illustrated is an example wireless networkperforming OTA software pushes to vehicles based on their locationaccording to one or more embodiments. OTA software pushes can also be afunction of location. A data repository 200 can store data related tocommunication between vehicles 214, 216, 218, and base stations 202,204, 206. The communication can comprise where the vehicles 214, 216,218 are in relation to the base stations 202, 204, 206. For instance,are the vehicles 214, 216, 218 within a defined zone 208, 2010, 212associated with the base stations 202, 204, 206. If the vehicles 214,216, 218 are within the defined zones 208, 2010, 212, then an OTAsoftware push can occur.

The OTA software push can also be predicated on a network capacityassociated with the base stations 202, 204, 206. The system candetermine not to send an OTA push via the base station 202 to thevehicle 214 because the base station 202 has been determined to be at ornear capacity. However, the system can send the vehicle 216 an OTAsoftware push via the base station 206 because the vehicle is within thedefined zone 210 even though the base station 206 might be nearcapacity. Effectively, the system can prioritize the OTA pushes based onplurality of factors such as: time, location, capacity, route,segmentation, frequency, reoccurrences, etc.

Referring now to FIG. 3, illustrated is a learning procedure of anexample wireless network performing OTA software scheduling according toone or more embodiments. Given a vector v from the learning algorithm, aschedule is built with respect to v, and then, the makespan or any othermetric is fed back into the learning algorithm. A schedule optimizer canbe composed of two components: 1) a learning machine and 2) a scheduler.The learning machine can first provide several vehicles'orderings/permutations to be used to build the schedule and send theorders to the scheduler. The scheduler can begin by sorting vehiclesaccording to some condition. After the schedule has been built for thescheduler, a makespan or other chosen metric can be fed back into thelearning machine to be use as a performance metric in the learningprocess. Then the scheduler can then build a schedule according to thesorting the vehicles, and evaluate the schedule according to themakespan prior to sending the metrics to the learning machine. Thescheduler can also work with a list of vehicles in a given orderprovided by the learning machine. For each vehicle in this order, thescheduler can allocate a download slot for that vehicle constrained tothe network metrics including, but not limited to: network capacity,location, time, etc. This process can be iterated several times until agiven stopping criterion is met.

Referring now to FIG. 4, illustrated is an example schematic systemblock diagram of an OTA scheduling process according to one or moreembodiments. A user identity 400 can leverage a scheduling website 402to facilitate an OTA software push. Based on the user identity 400 inputreceived by the scheduling website 402, various vehicles can besegmented based on criterion (time, capacity, bandwidth, location, etc.)set by the user identity 400. The vehicle segmentation can also comprisedata ingestion 404, which can be compared to any/all of the criterioninput by the user identity 400. After the vehicle segmentation 406, aheuristic scheduler 408 or an optimizing scheduler can be used togenerate an OTA software push schedule in accordance with the useridentity 400 inputs and the data ingestion 404. Additionally, once theschedule is generated, it can be published by a schedule publisher 412,which can then send the published schedule back to the schedulingwebsite 402.

Referring now to FIG. 5, illustrated is an example wireless networkarchitecture for OTA software scheduling for a vehicle according to oneor more embodiments. The process for an FOTA campaign, can entail anauto OEM sending a request for a FOTA campaign initiation to serviceprovider scheduler via an API. The request can contain informationconcerning the FOTA campaign that includes the FOTA package size, a listof targeted vehicles, and start and end dates of the campaign. Using therequest information, the vehicles' behavior can be derived from theservice provider's network data, the network load and performance data,and the service provider scheduler can compute the most effectiveschedule for the requested FOTA campaign. The schedule can comprise thelist of targeted vehicles and their optimal dates and times fordownloading the software update. Consequently, the service providerscheduler can invoke an API to transmit the computed schedule to theauto OEM. After the schedule is received, the auto OEM can begin to sendout short message service SMS notification to the targeted vehiclesbased on the computed download dates and times. The vehicle can thenreceive the SMS notification, acknowledge it, and initiate the softwaredownload from the auto OEM back office via wireless networkcommunication protocols associated with the vehicle.

Referring now to FIG. 6, illustrated is another example wireless networkarchitecture for OTA software scheduling for a vehicle according to oneor more embodiments. To initiate a FOTA campaign, an auto OEMrepresentative can contacts a service provider representative with theFOTA campaign request. The request can comprise information concerningthe FOTA campaign that comprises the FOTA package size, a list oftargeted cars, and start and end dates of the campaign. The serviceprovider representative can upload the campaign request information ontothe service provider scheduler web server. Using the requestinformation, the vehicles behavior can be derived from the serviceprovider network data, the network load and performance data, and theservice provider scheduler can compute the most effective schedule forthe requested FOTA campaign. The schedule can comprise the list oftargeted vehicles and their optimal dates and times for downloading thesoftware update. Once the schedule is completed, the service providerscheduler can notify the service provider representative of such. Theservice provider representative can then download the computed schedulefrom the service provider scheduler web server and share it with theauto OEM representative who can then upload the schedule onto the OEMback office. Once the schedule is received, the auto OEM can beginsending out SMS notifications to the targeted vehicles based on thecomputed download dates and times. The vehicle can receive the SMSnotification, acknowledge it, and initiate the software download fromthe auto OEM back office via wireless network communication protocolsassociated with the vehicle.

Referring now to FIG. 7, illustrated is an example schematic systemblock diagram for an OTA software scheduler component according to oneor more embodiments. The OTA software scheduler 700 can comprise areception component 702 for receiving data from a user identity and/or avehicle network activity data. An analysis component 704 can analyze andcompare the data received from the user identity to the vehicle networkactivity data, which can then be modeled via a modeling component 710. Alocation component 712 can be configured to track the location ofvehicles and a time allocation component 716 can be configured to trackthe amount of time vehicles communication with a specific base stationdevice. Consequently, time allocation data and location data can be usedas inputs to the analysis component 704 and the modeling component 710prior to a schedule generator component 706 generating an outputschedule. The schedule generator component 706 can also output vehiclesegmentation data and time allocation data. Data related to all of theaforementioned components can be stored in a memory component 708 foraccess and utilization at various stages of the OTA software schedulerprocess.

Referring now to FIG. 8, illustrated is an example schematic systemblock diagram for facilitating OTA software based on resource dataaccording to one or more embodiments. At element 800, network capacitydata representing a network capacity of network devices of a wirelessnetwork can be received. Base station devices can have a networkcapacity associated with their operation. The network capacity can bepredefined based on system requirements or it can be dynamic based on atime of day (i.e. commute times). At element 802, vehicle location dataassociated with a location of a vehicle in relation to a base stationdevice of the wireless network can be received. Vehicle location datacan be used to assess vehicle's day-to-day patterns andinteractions/communications with the base stations. Additionally,network connection data associated with a frequency of a radio of thevehicle connecting to the base station device can be received at element804, and, based on the network resource data, the vehicle location, andthe network connection data, resource schedule data associated with aschedule for a resource deliverable by the wireless network device to besent to the radio of the vehicle can be generated at element 806. Thus,the collected data can be used to model an efficient schedule for OTAsoftware pushes and issue OTA software in accordance with the schedule.

Referring now to FIG. 9, illustrated is an example schematic systemblock diagram for facilitating OTA software based on a segmentationprocess according to one or more embodiments. At element 900, a systemcan receive location data associated with locations of vehicles inrelation to a base station device associated with a wireless network.Location data and time data related to vehicle sessions on particularbase station devices can be used as inputs to the system. At element902, connection data associated with wireless connections of thevehicles to the base station device can be received. After theconnection data is received, it can be stored, analyzed against anydefined parameters and used to segment vehicles. Based on the locationdata and the connection data, the vehicles can be segmented at element904, resulting in a first vehicle segment and a second vehicle segment.The vehicle segmentation can dictate which vehicles should receive OTAsoftware downloads together/separately. In response to the segmentingthe vehicles, at element 906 a firmware download schedule associatedwith downloading firmware to the vehicles can be generated; and thefirmware can be sent to the vehicles in accordance with the firmwaredownload schedule at element 908.

Referring now to FIG. 10, illustrated is an example schematic systemblock for facilitating OTA software based on an adaptive schedulingprocess according to one or more embodiments. At element 1000, a firstorder associated with vehicles connected to network devices of awireless network can be generated, and at element 1002, first time slotsrelated to the first order can be allocated. An order can be associatedwith how OTA software pushes are sent to the vehicles, wherein eachvehicle can be provided a time slot for the OTA software push. Based onthe allocating the first time slots, a first schedule associated withsending executable instructions to the vehicles can be generated atelement 1004. The first schedule generated can be dynamically updated inaccordance with a data convergence process. In response to sending theexecutable instructions to the vehicles in accordance with firstschedule, network performance data associated with a performance of thenetwork devices of the wireless network can be analyzed at element 1006,resulting in a network analysis. The network analysis can determine thatthe first schedule was not or is not the most efficient schedulingprocess and consequently amend the schedule to reflect a more efficientscheduling process. Based on an output of the network analysis, a secondorder associated with the vehicles can be generated at element 1008. Atelement 1010, second time slots related to the second order can beallocated, and based on the allocating the second time slots, a secondschedule associated with sending the executable instructions to thevehicles can be generated at element 1012. Thereafter, the executableinstructions can be sent to the vehicles at element 1014 in accordancewith the second schedule to increase a metric relating to theperformance of the network devices of the wireless network.

Referring now to FIG. 11, illustrated is a schematic block diagram of anexemplary end-user device such as a mobile device 1100 capable ofconnecting to a network in accordance with some embodiments describedherein. Although a mobile handset 1100 is illustrated herein, it will beunderstood that other devices can be a mobile device, and that themobile handset 1100 is merely illustrated to provide context for theembodiments of the various embodiments described herein. The followingdiscussion is intended to provide a brief, general description of anexample of a suitable environment 1100 in which the various embodimentscan be implemented. While the description includes a general context ofcomputer-executable instructions embodied on a machine-readable storagemedium, those skilled in the art will recognize that the innovation alsocan be implemented in combination with other program modules and/or as acombination of hardware and software.

Generally, applications (e.g., program modules) can include routines,programs, components, data structures, etc., that perform particulartasks or implement particular abstract data types. Moreover, thoseskilled in the art will appreciate that the methods described herein canbe practiced with other system configurations, includingsingle-processor or multiprocessor systems, minicomputers, mainframecomputers, as well as personal computers, hand-held computing devices,microprocessor-based or programmable consumer electronics, and the like,each of which can be operatively coupled to one or more associateddevices.

A computing device can typically include a variety of machine-readablemedia. Machine-readable media can be any available media that can beaccessed by the computer and includes both volatile and non-volatilemedia, removable and non-removable media. By way of example and notlimitation, computer-readable media can comprise computer storage mediaand communication media. Computer storage media can include volatileand/or non-volatile media, removable and/or non-removable mediaimplemented in any method or technology for storage of information, suchas computer-readable instructions, data structures, program modules orother data. Computer storage media can include, but is not limited to,RAM, ROM, EEPROM, flash memory or other memory technology, CD ROM,digital video disk (DVD) or other optical disk storage, magneticcassettes, magnetic tape, magnetic disk storage or other magneticstorage devices, or any other medium which can be used to store thedesired information and which can be accessed by the computer.

Communication media typically embodies computer-readable instructions,data structures, program modules or other data in a modulated datasignal such as a carrier wave or other transport mechanism, and includesany information delivery media. The term “modulated data signal” means asignal that has one or more of its characteristics set or changed insuch a manner as to encode information in the signal. By way of example,and not limitation, communication media includes wired media such as awired network or direct-wired connection, and wireless media such asacoustic, RF, infrared and other wireless media. Combinations of the anyof the above should also be included within the scope ofcomputer-readable media.

The handset 1100 includes a processor 1102 for controlling andprocessing all onboard operations and functions. A memory 1104interfaces to the processor 1102 for storage of data and one or moreapplications 1106 (e.g., a video player software, user feedbackcomponent software, etc.). Other applications can include voicerecognition of predetermined voice commands that facilitate initiationof the user feedback signals. The applications 1106 can be stored in thememory 1104 and/or in a firmware 1108, and executed by the processor1102 from either or both the memory 1104 or/and the firmware 1108. Thefirmware 1108 can also store startup code for execution in initializingthe handset 1100. A communications component 1110 interfaces to theprocessor 1102 to facilitate wired/wireless communication with externalsystems, e.g., cellular networks, VoIP networks, and so on. Here, thecommunications component 1110 can also include a suitable cellulartransceiver 1111 (e.g., a GSM transceiver) and/or an unlicensedtransceiver 1113 (e.g., Wi-Fi, WiMax) for corresponding signalcommunications. The handset 1100 can be a device such as a cellulartelephone, a PDA with mobile communications capabilities, andmessaging-centric devices. The communications component 1110 alsofacilitates communications reception from terrestrial radio networks(e.g., broadcast), digital satellite radio networks, and Internet-basedradio services networks.

The handset 1100 includes a display 1112 for displaying text, images,video, telephony functions (e.g., a Caller ID function), setupfunctions, and for user input. For example, the display 1112 can also bereferred to as a “screen” that can accommodate the presentation ofmultimedia content (e.g., music metadata, messages, wallpaper, graphics,etc.). The display 1112 can also display videos and can facilitate thegeneration, editing and sharing of video quotes. A serial I/O interface1114 is provided in communication with the processor 1102 to facilitatewired and/or wireless serial communications (e.g., USB, and/or IEEE1394) through a hardwire connection, and other serial input devices(e.g., a keyboard, keypad, and mouse). This supports updating andtroubleshooting the handset 1100, for example. Audio capabilities areprovided with an audio I/O component 1116, which can include a speakerfor the output of audio signals related to, for example, indication thatthe user pressed the proper key or key combination to initiate the userfeedback signal. The audio I/O component 1116 also facilitates the inputof audio signals through a microphone to record data and/or telephonyvoice data, and for inputting voice signals for telephone conversations.

The handset 1100 can include a slot interface 1118 for accommodating aSIC (Subscriber Identity Component) in the form factor of a cardSubscriber Identity Module (SIM) or universal SIM 1120, and interfacingthe SIM card 1120 with the processor 1102. However, it is to beappreciated that the SIM card 1120 can be manufactured into the handset1100, and updated by downloading data and software.

The handset 1100 can process IP data traffic through the communicationcomponent 1110 to accommodate IP traffic from an IP network such as, forexample, the Internet, a corporate intranet, a home network, a personarea network, etc., through an ISP or broadband cable provider. Thus,VoIP traffic can be utilized by the handset 800 and IP-based multimediacontent can be received in either an encoded or decoded format.

A video processing component 1122 (e.g., a camera) can be provided fordecoding encoded multimedia content. The video processing component 1122can aid in facilitating the generation, editing and sharing of videoquotes. The handset 1100 also includes a power source 1124 in the formof batteries and/or an AC power subsystem, which power source 1124 caninterface to an external power system or charging equipment (not shown)by a power I/O component 1126.

The handset 1100 can also include a video component 1130 for processingvideo content received and, for recording and transmitting videocontent. For example, the video component 1130 can facilitate thegeneration, editing and sharing of video quotes. A location trackingcomponent 1132 facilitates geographically locating the handset 1100. Asdescribed hereinabove, this can occur when the user initiates thefeedback signal automatically or manually. A user input component 1134facilitates the user initiating the quality feedback signal. The userinput component 1134 can also facilitate the generation, editing andsharing of video quotes. The user input component 1134 can include suchconventional input device technologies such as a keypad, keyboard,mouse, stylus pen, and/or touch screen, for example.

Referring again to the applications 1106, a hysteresis component 1136facilitates the analysis and processing of hysteresis data, which isutilized to determine when to associate with the access point. Asoftware trigger component 1138 can be provided that facilitatestriggering of the hysteresis component 1138 when the Wi-Fi transceiver1113 detects the beacon of the access point. A SIP client 1140 enablesthe handset 1100 to support SIP protocols and register the subscriberwith the SIP registrar server. The applications 1106 can also include aclient 1142 that provides at least the capability of discovery, play andstore of multimedia content, for example, music.

The handset 1100, as indicated above related to the communicationscomponent 810, includes an indoor network radio transceiver 1113 (e.g.,Wi-Fi transceiver). This function supports the indoor radio link, suchas IEEE 802.11, for the dual-mode GSM handset 1100. The handset 1100 canaccommodate at least satellite radio services through a handset that cancombine wireless voice and digital radio chipsets into a single handhelddevice.

Referring now to FIG. 12, there is illustrated a block diagram of acomputer 1200 operable to execute a system architecture that facilitatesestablishing a transaction between an entity and a third party. Thecomputer 1200 can provide networking and communication capabilitiesbetween a wired or wireless communication network and a server and/orcommunication device. In order to provide additional context for variousaspects thereof, FIG. 12 and the following discussion are intended toprovide a brief, general description of a suitable computing environmentin which the various aspects of the innovation can be implemented tofacilitate the establishment of a transaction between an entity and athird party. While the description above is in the general context ofcomputer-executable instructions that can run on one or more computers,those skilled in the art will recognize that the innovation also can beimplemented in combination with other program modules and/or as acombination of hardware and software.

Generally, program modules include routines, programs, components, datastructures, etc., that perform particular tasks or implement particularabstract data types. Moreover, those skilled in the art will appreciatethat the methods can be practiced with other computer systemconfigurations, including single-processor or multiprocessor computersystems, minicomputers, mainframe computers, as well as personalcomputers, hand-held computing devices, microprocessor-based orprogrammable consumer electronics, and the like, each of which can beoperatively coupled to one or more associated devices.

The illustrated aspects of the innovation can also be practiced indistributed computing environments where certain tasks are performed byremote processing devices that are linked through a communicationsnetwork. In a distributed computing environment, program modules can belocated in both local and remote memory storage devices.

Computing devices typically include a variety of media, which caninclude computer-readable storage media or communications media, whichtwo terms are used herein differently from one another as follows.

Computer-readable storage media can be any available storage media thatcan be accessed by the computer and includes both volatile andnonvolatile media, removable and non-removable media. By way of example,and not limitation, computer-readable storage media can be implementedin connection with any method or technology for storage of informationsuch as computer-readable instructions, program modules, structureddata, or unstructured data. Computer-readable storage media can include,but are not limited to, RAM, ROM, EEPROM, flash memory or other memorytechnology, CD-ROM, digital versatile disk (DVD) or other optical diskstorage, magnetic cassettes, magnetic tape, magnetic disk storage orother magnetic storage devices, or other tangible and/or non-transitorymedia which can be used to store desired information. Computer-readablestorage media can be accessed by one or more local or remote computingdevices, e.g., via access requests, queries or other data retrievalprotocols, for a variety of operations with respect to the informationstored by the medium.

Communications media can embody computer-readable instructions, datastructures, program modules or other structured or unstructured data ina data signal such as a modulated data signal, e.g., a carrier wave orother transport mechanism, and includes any information delivery ortransport media. The term “modulated data signal” or signals refers to asignal that has one or more of its characteristics set or changed insuch a manner as to encode information in one or more signals. By way ofexample, and not limitation, communication media include wired media,such as a wired network or direct-wired connection, and wireless mediasuch as acoustic, RF, infrared and other wireless media.

With reference to FIG. 12, implementing various aspects described hereinwith regards to the end-user device can include a computer 1200, thecomputer 1200 including a processing unit 1204, a system memory 1206 anda system bus 1208. The system bus 1208 couples system componentsincluding, but not limited to, the system memory 1206 to the processingunit 1204. The processing unit 1204 can be any of various commerciallyavailable processors. Dual microprocessors and other multi processorarchitectures can also be employed as the processing unit 1204.

The system bus 1208 can be any of several types of bus structure thatcan further interconnect to a memory bus (with or without a memorycontroller), a peripheral bus, and a local bus using any of a variety ofcommercially available bus architectures. The system memory 1206includes read-only memory (ROM) 1227 and random access memory (RAM)1212. A basic input/output system (BIOS) is stored in a non-volatilememory 1227 such as ROM, EPROM, EEPROM, which BIOS contains the basicroutines that help to transfer information between elements within thecomputer 1200, such as during start-up. The RAM 1212 can also include ahigh-speed RAM such as static RAM for caching data.

The computer 1200 further includes an internal hard disk drive (HDD)1214 (e.g., EIDE, SATA), which internal hard disk drive 1214 can also beconfigured for external use in a suitable chassis (not shown), amagnetic floppy disk drive (FDD) 1216, (e.g., to read from or write to aremovable diskette 1218) and an optical disk drive 1220, (e.g., readinga CD-ROM disk 1222 or, to read from or write to other high capacityoptical media such as the DVD). The hard disk drive 1214, magnetic diskdrive 1216 and optical disk drive 1220 can be connected to the systembus 1208 by a hard disk drive interface 1224, a magnetic disk driveinterface 1226 and an optical drive interface 1228, respectively. Theinterface 1224 for external drive implementations includes at least oneor both of Universal Serial Bus (USB) and IEEE 1294 interfacetechnologies. Other external drive connection technologies are withincontemplation of the subject innovation.

The drives and their associated computer-readable media providenonvolatile storage of data, data structures, computer-executableinstructions, and so forth. For the computer 1200 the drives and mediaaccommodate the storage of any data in a suitable digital format.Although the description of computer-readable media above refers to aHDD, a removable magnetic diskette, and a removable optical media suchas a CD or DVD, it should be appreciated by those skilled in the artthat other types of media which are readable by a computer 1200, such aszip drives, magnetic cassettes, flash memory cards, cartridges, and thelike, can also be used in the exemplary operating environment, andfurther, that any such media can contain computer-executableinstructions for performing the methods of the disclosed innovation.

A number of program modules can be stored in the drives and RAM 1212,including an operating system 1230, one or more application programs1232, other program modules 1234 and program data 1236. All or portionsof the operating system, applications, modules, and/or data can also becached in the RAM 1212. It is to be appreciated that the innovation canbe implemented with various commercially available operating systems orcombinations of operating systems.

A user can enter commands and information into the computer 1200 throughone or more wired/wireless input devices, e.g., a keyboard 1238 and apointing device, such as a mouse 1240. Other input devices (not shown)may include a microphone, an IR remote control, a joystick, a game pad,a stylus pen, touch screen, or the like. These and other input devicesare often connected to the processing unit 1204 through an input deviceinterface 1242 that is coupled to the system bus 1208, but can beconnected by other interfaces, such as a parallel port, an IEEE 2394serial port, a game port, a USB port, an IR interface, etc.

A monitor 1244 or other type of display device is also connected to thesystem bus 1208 through an interface, such as a video adapter 1246. Inaddition to the monitor 1244, a computer 1200 typically includes otherperipheral output devices (not shown), such as speakers, printers, etc.

The computer 1200 can operate in a networked environment using logicalconnections by wired and/or wireless communications to one or moreremote computers, such as a remote computer(s) 1248. The remotecomputer(s) 1248 can be a workstation, a server computer, a router, apersonal computer, portable computer, microprocessor-based entertainmentdevice, a peer device or other common network node, and typicallyincludes many or all of the elements described relative to the computer,although, for purposes of brevity, only a memory/storage device 1250 isillustrated. The logical connections depicted include wired/wirelessconnectivity to a local area network (LAN) 1252 and/or larger networks,e.g., a wide area network (WAN) 1254. Such LAN and WAN networkingenvironments are commonplace in offices and companies, and facilitateenterprise-wide computer networks, such as intranets, all of which mayconnect to a global communications network, e.g., the Internet.

When used in a LAN networking environment, the computer 1200 isconnected to the local network 1252 through a wired and/or wirelesscommunication network interface or adapter 1256. The adapter 1256 mayfacilitate wired or wireless communication to the LAN 1252, which mayalso include a wireless access point disposed thereon for communicatingwith the wireless adapter 1256.

When used in a WAN networking environment, the computer 1200 can includea modem 1258, or is connected to a communications server on the WAN1254, or has other means for establishing communications over the WAN1254, such as by way of the Internet. The modem 1258, which can beinternal or external and a wired or wireless device, is connected to thesystem bus 1208 through the input device interface 1242. In a networkedenvironment, program modules depicted relative to the computer, orportions thereof, can be stored in the remote memory/storage device1250. It will be appreciated that the network connections shown areexemplary and other means of establishing a communications link betweenthe computers can be used.

The computer is operable to communicate with any wireless devices orentities operatively disposed in wireless communication, e.g., aprinter, scanner, desktop and/or portable computer, portable dataassistant, communications satellite, any piece of equipment or locationassociated with a wirelessly detectable tag (e.g., a kiosk, news stand,restroom), and telephone. This includes at least Wi-Fi and Bluetooth™wireless technologies. Thus, the communication can be a predefinedstructure as with a conventional network or simply an ad hoccommunication between at least two devices.

Wi-Fi, or Wireless Fidelity, allows connection to the Internet from acouch at home, a bed in a hotel room, or a conference room at work,without wires. Wi-Fi is a wireless technology similar to that used in acell phone that enables such devices, e.g., computers, to send andreceive data indoors and out; anywhere within the range of a basestation. Wi-Fi networks use radio technologies called IEEE 802.11 (a, b,g, etc.) to provide secure, reliable, fast wireless connectivity. AWi-Fi network can be used to connect computers to each other, to theInternet, and to wired networks (which use IEEE 802.3 or Ethernet).Wi-Fi networks operate in the unlicensed 2.4 and 5 GHz radio bands, atan 11 Mbps (802.11a) or 54 Mbps (802.11b) data rate, for example, orwith products that contain both bands (dual band), so the networks canprovide real-world performance similar to the basic 10BaseT wiredEthernet networks used in many offices.

The above description of illustrated embodiments of the subjectdisclosure, including what is described in the Abstract, is not intendedto be exhaustive or to limit the disclosed embodiments to the preciseforms disclosed. While specific embodiments and examples are describedherein for illustrative purposes, various modifications are possiblethat are considered within the scope of such embodiments and examples,as those skilled in the relevant art can recognize.

In this regard, while the subject matter has been described herein inconnection with various embodiments and corresponding FIGS., whereapplicable, it is to be understood that other similar embodiments can beused or modifications and additions can be made to the describedembodiments for performing the same, similar, alternative, or substitutefunction of the disclosed subject matter without deviating therefrom.Therefore, the disclosed subject matter should not be limited to anysingle embodiment described herein, but rather should be construed inbreadth and scope in accordance with the appended claims below.

What is claimed is:
 1. A method, comprising: receiving, by networkequipment comprising a processor, network connection data associatedwith a frequency of a radio of a vehicle connecting to base stationequipment, wherein the network connection data is utilized by thevehicle during a defined commute time; in response to receiving thenetwork connection data that the vehicle utilizes during the commutetime, generating, by the network equipment, segmentation data associatedwith grouping the vehicle with a group of vehicles based on the networkconnection data and the commute time; generating, by the networkequipment, traffic pattern data associated with traffic patterns of thegroup of vehicles; based on the frequency of the radio of the vehicleconnecting to the base station equipment, the commute time of thevehicle, and the segmentation data, generating, priority data associatedwith a priority of the vehicle to receive a resource to be delivered tothe vehicle, wherein the resource comprises firmware applicable for useby the vehicle; and based on network capacity data, the traffic patterndata, and the priority data, generating, by the network equipment,during a peak time, resource schedule data associated with a schedulefor the resource deliverable by the network equipment to be sent to theradio of the vehicle.
 2. The method of claim 1, further comprising:receiving, by the network equipment, the network capacity datarepresenting a network capacity associated with a network comprising thenetwork equipment.
 3. The method of claim 1, further comprising:receiving, by the network equipment vehicle location data associatedwith a location of the vehicle in relation to the base stationequipment.
 4. The method of claim 1, wherein generating the resourceschedule data is performed during the peak time associated with anetwork capacity associated with the network capacity data.
 5. Themethod of claim 1, further comprising: in response to generating theresource schedule data, sending, by the network equipment, the firmwareto the vehicle.
 6. The method of claim 1, wherein the resource scheduledata is generated during the peak time for travel of the vehicle duringa weekend.
 7. The method of claim 1, wherein the resource schedule datais modeled as a function of an hourly network utilization pattern.
 8. Asystem, comprising: a processor; and a memory that stores executableinstructions that, when executed by the processor, facilitateperformance of operations, comprising: receiving network connection dataassociated with a frequency of a radio of a vehicle connecting to a basestation, the receiving being during a commute time; based on receivingthe network connection data and the commute time: grouping the vehiclein a group of vehicles, resulting in grouping data, and generatingpriority data associated with a priority of the group of the vehicles toreceive firmware; and generating traffic pattern data associated withtraffic patterns of the group of vehicles; and based on network capacitydata representative of a network capacity of network devices, vehiclelocation data associated with a location of the vehicle in relation tothe base station, and the commute time, generating, during a peak time,a firmware download schedule associated with downloading the firmware tothe group of vehicles.
 9. The system of claim 8, wherein generating thefirmware download schedule is further based on the traffic pattern data.10. The system of claim 8, wherein generating the firmware downloadschedule is further based on the priority data.
 11. The system of claim8, wherein generating the firmware download schedule is further based onthe grouping data.
 12. The system of claim 8, wherein the operationsfurther comprise: receiving the network capacity data, from the basestation, during the peak time.
 13. The system of claim 8, wherein theoperations further comprise: receiving the vehicle location data, fromthe vehicle, during the peak time.
 14. The system of claim 8, whereinthe operations further comprise: sending the firmware to the group ofvehicles, wherein the sending comprises sending the firmware to thevehicle of a vehicle segment based on an indication that the vehicle ofthe vehicle segment wirelessly connects to the base station during anoff-peak time.
 15. A non-transitory machine-readable medium, comprisingexecutable instructions that, when executed by a processor, facilitateperformance of operations, comprising: receiving network connection dataassociated with a frequency of a radio of a vehicle connecting to a basestation device, wherein the vehicle is configured to utilize the networkconnection data during a commute time; based on the frequency,generating priority data associated with a priority of the vehicle toreceive a resource to be delivered to the vehicle, wherein the resourceis firmware configured to be installed for use by the vehicle; based onreceiving the network connection data, grouping the vehicle in a groupof vehicles, resulting in grouping data; based on network capacity datarepresentative of a network capacity, vehicle location datarepresentative of a location of the vehicle in relation to the basestation device, the commute time of the vehicle, and the grouping data,generating, during a peak time, a schedule associated with sendinginstruction data to the group vehicles; and based on the schedule,sending the firmware to the vehicle.
 16. The non-transitorymachine-readable medium of claim 15, wherein the operations furthercomprise: analyzing network performance data associated with aperformance of network devices of the wireless network.
 17. Thenon-transitory machine-readable medium of claim 15, wherein theoperations further comprise: generating traffic pattern data associatedwith traffic patterns of the group of vehicles, wherein the trafficpattern data is further used to generate the schedule.
 18. Thenon-transitory machine-readable medium of claim 15, wherein the sendingthe instruction data comprises: sending the instruction data to thegroup of vehicles in accordance with the schedule to increase a metricrelating to a performance of network devices of the wireless network.19. The non-transitory machine-readable medium of claim 15, wherein theoperations further comprise: in response to determining that a conditionassociated with a time has been satisfied, generating a first definedorder related to allocating a time slot to the vehicle.
 20. Thenon-transitory machine-readable medium of claim 15, wherein theoperations further comprise: receiving vehicle location data associatedwith a location of the vehicle in relation to the base station device ofthe wireless network.